[go: up one dir, main page]

Brooking, 1996 - Google Patents

An artificial neural network based on biological principles

Brooking, 1996

Document ID
17763054003339662900
Author
Brooking G
Publication year

External Links

Snippet

The successfulness of biological systems have encouraged the investigation and development of some of these biological principles into engineering applications. This area of development has been termed artificial neural networks. These artificial neural networks …
Continue reading at search.proquest.com (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G06N3/0635—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G06N3/082—Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • G06N3/049—Temporal neural nets, e.g. delay elements, oscillating neurons, pulsed inputs
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • G06N3/0472—Architectures, e.g. interconnection topology using probabilistic elements, e.g. p-rams, stochastic processors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G06N3/086—Learning methods using evolutionary programming, e.g. genetic algorithms
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/10—Simulation on general purpose computers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/004—Artificial life, i.e. computers simulating life
    • G06N3/008—Artificial life, i.e. computers simulating life based on physical entities controlled by simulated intelligence so as to replicate intelligent life forms, e.g. robots replicating pets or humans in their appearance or behavior
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/12—Computer systems based on biological models using genetic models
    • G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system

Similar Documents

Publication Publication Date Title
Bellec et al. Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets
Medler A brief history of connectionism
US8504502B2 (en) Prediction by single neurons
KR100567465B1 (en) Dynamic Synapses for Signal Processing in Neural Networks
Fiete et al. Temporal sparseness of the premotor drive is important for rapid learning in a neural network model of birdsong
Ivancevic et al. Quantum neural computation
Pollack Connectionism: Past, present, and future
Ponulak Supervised learning in spiking neural networks with ReSuMe method
Alvado et al. Hardware computation of conductance-based neuron models
Maass et al. Theory of the computational function of microcircuit dynamics
Wennekers¹ et al. Modelling generic cognitive functions with operational Hebbian cell assemblies
Brooking An artificial neural network based on biological principles
Kozachkov et al. Sequence learning in associative neuronal-astrocytic networks
Yashchenko Neural-like growing networks in the development of general intelligence. Neural-like growing networks (P. II)
Burgsteiner On learning with recurrent spiking neutral networks and their applications to robot control with real-world devices
Bennington et al. Towards biophysical network simulation of stochastically-formed neurospheres
Poon et al. Hebbian learning in parallel and modular memories
Jimenez-Romero A Heterosynaptic Spiking Neural System for the Development of Autonomous Agents
Gort Vicente A bridge from neuroscientific models to recurrent neural networks. Derivation of continuous-time connectionist models from neuroscience computational principles
Bamford Synaptic rewiring in neuromorphic VLSI for topographic map formation
Rowe Neural networks: a bridge between neuroscience and psychology
Romero A Heterosynaptic Spiking Neural System for the Development of Autonomous Agents
González-Nalda et al. Topos: Spiking neural networks for temporal pattern recognition in complex real sounds
Schwarcz Application of Diffuse Neuromodulation To Deep-Learning Networks
Zeng Learning Algorithm Designs for Biological Neural Networks